Technologies for diagnosing neurological or psychiatric illnesses

ABSTRACT

A technology which enables identifying, via a computer, a vessel in a third image. The third image is obtained from a subtraction of a second image from a first image. The second image and the first image are aligned within an imaging space. The first image is post-contrast. The second image is pre-contrast. The technology enables determining, via the computer, a voxel intensity mean value of a segment of the vessel in the third image. The technology enables obtaining, via the computer, a fourth image from a division of the third image by the voxel intensity mean value. The technology enables applying, via the computer, a filter onto the fourth image. The technology enables generating, via the computer, a filter mask based on the fourth image.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application in a Continuation of U.S. Nonprovisionalapplication Ser. No. 15/304,611 filed 17 Apr. 2015; which claims thebenefit of priority to PCT International Application No.PCT/US2015/026523 filed 17 Apr. 2015; which claims the benefit of U.S.Provisional Application Ser. No. 61/981,005 filed 17 Apr. 2014, each ofwhich is incorporated herein by reference in its entirety for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under grant MH093398awarded by the National Institutes of Health. The government has certainrights in the invention.

BACKGROUND

In the present disclosure, where a document, an act and/or an item ofknowledge is referred to and/or discussed, then such reference and/ordiscussion is not an admission that the document, the act and/or theitem of knowledge and/or any combination thereof was at the prioritydate, publicly available, known to the public, part of common generalknowledge and/or otherwise constitutes prior art under the applicablestatutory provisions; and/or is known to be relevant to an attempt tosolve any problem with which the present disclosure is concerned with.Further, nothing is disclaimed.

Magnetic resonance imaging (MRI) is a technology which has many uses.One of such uses is human brain imaging for research purposes or medicalpurposes. Functional MRI (FMRI) is a type of MRI which measures brainactivity based on detecting changes in blood oxygen content or bloodflow, where the changes result from neural activity of the brain.

Schizophrenia is a psychiatric disorder which afflicts from about 0.5%to about 1% of US adults. Consequently, earlier confirmed schizophreniadiagnosis can lead to fewer symptoms, greater chance for treatmentresponse, or lower medical costs, especially cumulatively. However,confirmation of such diagnosis through traditional subjective methods,including responsiveness to antipsychotic medication and follow-upevaluation, can be costly and time-consuming, such as for severalmonths. Furthermore, there is no established objective method that hasbeen shown to delineate schizophrenia and other similar diseases.Additionally, there is no established objective method to determinewhich schizophrenia patients will convert to an advanced stage ofpsychosis when first evaluated by a healthcare provider, such as apsychiatrist. As a result, the healthcare provider does not know whichof the schizophrenia patients will convert to a more advanced state ofpsychosis, or how well a targeted treatment is working. As shown in FIG.1, a cycle of care for a schizophrenia patient with suspected psychosisentails a battery of tests, evaluations, and follow-ups, as well as longterm care. Accordingly, there is no established objective method toprovide a first-line evaluation to determine which schizophreniapatients are at greater risk of converting to a more advanced state ofpsychosis or schizophrenia.

Alzheimer's disease has been linked to entorhinal cortex dysfunction. Toresearch or diagnose how Alzheimer's disease affects the entorhinalcortex, a high resolution, metabolism sensitive, and reliable imagingvariant is desired. Cerebral blood volume (CBV) is one such variant.However, CBV often relies on manual labeling of relevant regions ofinterest (ROI), which cannot distinguish various entorhinal cortex areaswithout reliable anatomical landmarks.

BRIEF SUMMARY

The present disclosure at least partially addresses a limitation ofexisting systems and methods. However, the present disclosure can proveuseful to other technical areas. Therefore, the claims should not beconstrued as necessarily limited to addressing any of the above.

One embodiment comprises a method comprising identifying, via acomputer, a vessel in a third image, wherein the third image is obtainedfrom a subtraction of a second image from a first image, wherein thesecond image and the first image are aligned within an imaging space,wherein the first image is post-contrast, wherein the second image ispre-contrast; determining, via the computer, a voxel intensity meanvalue of a segment of the vessel in the third image; obtaining, via thecomputer, a fourth image from a division of the third image by the voxelintensity mean value; applying, via the computer, a filter onto thefourth image; generating, via the computer, a filter mask based on thefourth image.

In the one embodiment, the method may further comprise performing, viathe computer, a vessel segmentation process on at least one of the firstimage or the second image before the identifying.

In the one embodiment, the method may further comprise wherein theperforming is automatically triggered.

In the one embodiment, the method may further comprise wherein thevessel comprises a diameter of about one centimeter or less.

In the one embodiment, the method may further comprise wherein theidentifying is based on a vesselness filter and a pre-defined region ofinterest, wherein the vesselness filter filters based on a set of eigenvalues of a Hessian matrix of the third image, wherein the third imageis modified such that the region of interest is positioned in apredefined area.

In the one embodiment, the method may further comprise wherein the voxelintensity mean value is based on a highest voxel intensity range in thesegment, wherein the range comprises the top 40% of voxel intensities.

In the one embodiment, the method may further comprise wherein the rangecomprises the top 33% of voxel intensities.

In the one embodiment, the method may further comprise wherein the rangecomprises the top 25% of voxel intensities.

In the one embodiment, the method may further comprise wherein the thirdimage is a cerebral blood volume map, wherein the filter is based on atleast one of a performance of an expectation-maximization segmentation,or a fitting of a bimodal Gaussian curve to a histogram of data inaccordance with the third image.

In the one embodiment, the method may further comprise wherein thefilter mask is a binary mask, and further comprising: applying, via thecomputer, the binary mask to the third image; mapping, via the computer,based on the applying, the third image according to a change in atransverse relaxation time induced via an input of a contrasting agent.

Another embodiment comprises a system comprising a hardware processorand a memory coupled to the hardware processor. The memory stores a setof instructions to execute via the hardware processor. The instructionsinstruct the hardware processor to perform a method comprisingidentifying, via a computer, a vessel in a third image, wherein thethird image is obtained from a subtraction of a second image from afirst image, wherein the second image and the first image are alignedwithin an imaging space, wherein the first image is post-contrast,wherein the second image is pre-contrast; determining, via the computer,a voxel intensity mean value of a segment of the vessel in the thirdimage; obtaining, via the computer, a fourth image from a division ofthe third image by the voxel intensity mean value; applying, via thecomputer, a filter onto the fourth image; generating, via the computer,a filter mask based on the fourth image.

In the another embodiment, the method may further comprise performing,via the computer, a vessel segmentation process on at least one of thefirst image or the second image before the identifying.

In the another embodiment, the method may further comprise wherein theperforming is automatically triggered.

In the another embodiment, the method may further comprise wherein thevessel comprises a diameter of about one centimeter or less.

In the another embodiment, the method may further comprise wherein theidentifying is based on a vesselness filter and a pre-defined region ofinterest, wherein the vesselness filter filters based on a set of eigenvalues of a Hessian matrix of the third image, wherein the third imageis modified such that the region of interest is positioned in apredefined area.

In the another embodiment, the method may further comprise wherein thevoxel intensity mean value is based on a highest voxel intensity rangein the segment, wherein the range comprises the top 40% of voxelintensities.

In the another embodiment, the method may further comprise wherein therange comprises the top 33% of voxel intensities.

In the another embodiment, the method may further comprise wherein therange comprises the top 25% of voxel intensities.

In the another embodiment, the method may further comprise wherein thethird image is a cerebral blood volume map, wherein the filter is basedon at least one of a performance of an expectation-maximizationsegmentation, or a fitting of a bimodal Gaussian curve to a histogram ofdata in accordance with the third image.

In the another embodiment, the method may further comprise wherein thefilter mask is a binary mask, and further comprising: applying, via thecomputer, the binary mask to the third image; mapping, via the computer,based on the applying, the third image according to a change in atransverse relaxation time induced via an input of a contrasting agent.

Yet another embodiment comprises a computer-readable storage devicestoring a set of instructions for execution via a processing circuit toimplement a method, wherein the method comprising: identifying, via acomputer, a vessel in a third image, wherein the third image is obtainedfrom a subtraction of a second image from a first image, wherein thesecond image and the first image are aligned within an imaging space,wherein the first image is post-contrast, wherein the second image ispre-contrast; determining, via the computer, a voxel intensity meanvalue of a segment of the vessel in the third image; obtaining, via thecomputer, a fourth image from a division of the third image by the voxelintensity mean value; applying, via the computer, a filter onto thefourth image; generating, via the computer, a filter mask based on thefourth image.

In the yet another embodiment, the method may further compriseperforming, via the computer, a vessel segmentation process on at leastone of the first image or the second image before the identifying.

In the yet another embodiment, the method may further comprise whereinthe performing is automatically triggered.

In the yet another embodiment, the method may further comprise whereinthe vessel comprises a diameter of about one centimeter or less.

In the yet another embodiment, the method may further comprise whereinthe identifying is based on a vesselness filter and a pre-defined regionof interest, wherein the vesselness filter filters based on a set ofeigen values of a Hessian matrix of the third image, wherein the thirdimage is modified such that the region of interest is positioned in apredefined area.

In the yet another embodiment, the method may further comprise whereinthe voxel intensity mean value is based on a highest voxel intensityrange in the segment, wherein the range comprises the top 40% of voxelintensities.

In the yet another embodiment, the method may further comprise whereinthe range comprises the top 33% of voxel intensities.

In the yet another embodiment, the method may further comprise whereinthe range comprises the top 25% of voxel intensities.

In the yet another embodiment, the method may further comprise whereinthe third image is a cerebral blood volume map, wherein the filter isbased on at least one of a performance of an expectation-maximizationsegmentation, or a fitting of a bimodal Gaussian curve to a histogram ofdata in accordance with the third image.

In the yet another embodiment, the method may further comprise whereinthe filter mask is a binary mask, and further comprising: applying, viathe computer, the binary mask to the third image; mapping, via thecomputer, based on the applying, the third image according to a changein a transverse relaxation time induced via an input of a contrastingagent.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate example embodiments of the presentdisclosure. Such drawings are not to be construed as necessarilylimiting the disclosure. Like numbers and/or similar numbering schemecan refer to like and/or similar elements throughout.

FIG. 1 shows a flowchart of a cycle of care for a schizophrenia patientaccording to the present disclosure.

FIG. 2 shows a flowchart of an example embodiment of a process for imageprocessing according to the present disclosure.

FIG. 3A shows an example X-Y diagram indicative of a signal increase ina post contrast image according to the present disclosure.

FIG. 3B shows an example histogram of the X-Y diagram of FIG. 3A,according to the present disclosure.

FIG. 4A shows an example embodiment of an image with several connectedcomponents which are unlabeled according to the present disclosure.

FIG. 4B shows an example embodiment of an image with several connectedcomponents which are labeled based on color or shading of contiguousvoxels according to the present disclosure.

FIG. 5A shows an example embodiment of a frontal view of a rawunbinarized vesselness mask according to the present disclosure.

FIG. 5B shows an example embodiment of a sagittal side view of a rawunbinarized vesselness mask according to the present disclosure.

FIG. 5C shows an example embodiment of a top view of a raw unbinarizedvesselness mask according to the present disclosure.

FIG. 5D shows an example embodiment of an orthographic projection viewof a raw unbinarized vesselness mask according to the presentdisclosure.

FIG. 6A shows an example embodiment of a frontal view of a connectedcomponent mask according to the present disclosure.

FIG. 6B shows an example embodiment of a sagittal side view of aconnected component mask according to the present disclosure.

FIG. 6C shows an example embodiment of a top view of a connectedcomponent mask according to the present disclosure.

FIG. 6D shows an example embodiment of an orthographic projection viewof a connected component mask according to the present disclosure.

FIG. 7 shows an example embodiment of a ranking of likelihood of anisolated mask compared to a probabilistic atlas according to the presentdisclosure.

FIG. 8A shows an example embodiment of a frontal view of a probabilisticatlas in a standard space according to the present disclosure.

FIG. 8B shows an example embodiment of a sagittal side view of aprobabilistic atlas in a standard space according to the presentdisclosure.

FIG. 8C shows an example embodiment of a top view of a probabilisticatlas in a standard space according to the present disclosure.

FIG. 9A shows an example embodiment of a frontal view of a vessel atlasimage on a template brain according to the present disclosure.

FIG. 9B shows an example embodiment of a sagittal side view of a vesselatlas image on a template brain according to the present disclosure.

FIG. 9C shows an example embodiment of a top view of a vessel atlasimage on a template brain according to the present disclosure.

FIG. 10 shows an example embodiment of an atlas generation and atlasapplication diagram according to the present disclosure.

FIG. 11A shows an example embodiment of a CBV map on an anatomical spaceaccording to the present disclosure.

FIG. 11B shows an example embodiment of a CBV map labeled based onregions according to anatomical locations on an anatomical spaceaccording to the present disclosure.

FIG. 11C shows an example embodiment of a CBV map with vessel filters onan anatomical space according to the present disclosure.

FIG. 12 shows a diagram of an example embodiment of a table ofstatistics according to the present disclosure.

FIG. 13 shows a perspective view of an example embodiment of a CBV ROIdiscriminating between controls and preclinical Alzheimer' patientsaccording to the present disclosure.

FIG. 14 shows a flowchart of an example embodiment of a process forimage processing according to the present disclosure.

FIG. 15A shows a perspective view of an example embodiment of a desktopcomputer according to the present disclosure.

FIG. 15B shows a schematic view of an example embodiment of the desktopcomputer according to the present disclosure.

FIG. 16 shows an example screenshot of a software application forcerebral blood volume generation according to the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present disclosure is now described more fully with reference to theaccompanying drawings, in which example embodiments of the presentdisclosure are shown. The present disclosure can, however, be embodiedin many different forms and should not be construed as necessarily beinglimited to the example embodiments disclosed herein. Rather, theseexample embodiments are provided so that the present disclosure isthorough and complete, and fully conveys the concepts of the presentdisclosure to those skilled in the relevant art. In addition, featuresdescribed with respect to certain example embodiments can be combined inand/or with various other example embodiments. Different aspects and/orelements of example embodiments, as disclosed herein, can be combined ina similar manner.

The terminology used herein can imply direct or indirect, full orpartial, temporary or permanent, action or inaction. For example, whenan element is referred to as being “on,” “connected” or “coupled” toanother element, then the element can be directly on, connected orcoupled to the other element and/or intervening elements can be present,including indirect and/or direct variants. In contrast, when an elementis referred to as being “directly connected” or “directly coupled” toanother element, there are no intervening elements present.

Although the terms first, second, etc. can be used herein to describevarious elements, components, regions, layers and/or sections, theseelements, components, regions, layers and/or sections should notnecessarily be limited by such terms. These terms are used todistinguish one element, component, region, layer or section fromanother element, component, region, layer or section. Thus, a firstelement, component, region, layer, or section discussed below could betermed a second element, component, region, layer, or section withoutdeparting from the teachings of the present disclosure.

The terminology used herein is for describing particular exampleembodiments and is not intended to be necessarily limiting of thepresent disclosure. As used herein, the singular forms “a,” “an” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. The terms “comprises,” “includes”and/or “comprising,” “including” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence and/oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure belongs. Theterms, such as those defined in commonly used dictionaries, should beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art and should not be interpreted in anidealized and/or overly formal sense unless expressly so defined herein.

Furthermore, relative terms such as “below,” “lower,” “above,” and“upper” can be used herein to describe one element's relationship toanother element as illustrated in the accompanying drawings. Suchrelative terms are intended to encompass different orientations ofillustrated technologies in addition to the orientation depicted in theaccompanying drawings. For example, if a device in the accompanyingdrawings were turned over, then the elements described as being on the“lower” side of other elements would then be oriented on “upper” sidesof the other elements. Similarly, if the device in one of the figureswere turned over, elements described as “below” or “beneath” otherelements would then be oriented “above” the other elements. Therefore,the example terms “below” and “lower” can encompass both an orientationof above and below.

As used herein, the term “or” is intended to mean an inclusive “or”rather than an exclusive “or.” That is, unless specified otherwise, orclear from context, “X employs A or B” is intended to mean any of thenatural inclusive permutations. That is, if X employs A; X employs B; orX employs both A and B, then “X employs A or B” is satisfied under anyof the foregoing instances.

As used herein, the term “about” and/or “substantially” refers to a+/−10% variation from the nominal value/term. Such variation is alwaysincluded in any given value/term provided herein, whether or not suchvariation is specifically referred thereto.

If any disclosures are incorporated herein by reference and suchdisclosures conflict in part and/or in whole with the presentdisclosure, then to the extent of conflict, and/or broader disclosure,and/or broader definition of terms, the present disclosure controls. Ifsuch disclosures conflict in part and/or in whole with one another, thento the extent of conflict, the later-dated disclosure controls.

In one aspect, a system provides a first-line evaluation of whichpatients are at greater risk of converting to a more advanced state ofpsychosis or schizophrenia. For example, such system can comprisemethods and systems which employ software that can automaticallygenerate a variant of FMRI derived from routine brain MRI sequences i.e.CBV, which is a correlate of brain activity because such values arealtered in schizophrenia. More specifically, glutamate dyshomeostasis,specifically in the CA1 sub-region of a patient's hippocampus, seems tobe able to correlate to conversion of at-risk patients to more advancedpsychosis. Thus, changes in glutamate, a neurotransmitter, have beenshown to correlate to schizophrenia and psychosis diagnosis. These CBVvalues provide objective data that can inform healthcare providers, suchas psychiatrists, or caregivers, that should lead to a quicker or moreaccurate diagnosis. For another example, an image processing method canidentify glutamate dyshomeostasis values in an efficient and reliablemanner. Thus, in many cases, a healthcare provider, such as a physician,may be able to order a brain MRI as a matter of practice in thesesusceptible groups to rule out a presence of brain lesions. For yetanother example, a healthcare provider, such as a physician, could ordera CBV scan with relatively minimal, if any, extra effort for a patientor MRI site, and a result of such scan with CBV values can be generatedin a few hours. Accordingly, if a patient were to receive a CBV scan,then a set of statistics could be generated for cranial regionscorresponding to a severity of such disease. For many people who have atleast one psychotic episode per year, this information would givehealthcare providers, such as psychiatrists, or treatment facilitiesmore information and potentially reduce the amount of time needed for ahealthcare provider, such as a psychiatrist, to confirm a schizophreniadiagnosis.

FIG. 2 shows a flowchart of an example embodiment of a process for imageprocessing according to the present disclosure. A process 200 is usedfor image processing, as described herein. The process 200 is performedvia at least one actor, such as via a user operating a computer of FIG.17A. For example, the user can be a healthcare provider, such as aphysician, a psychiatrist, a nurse, or a technician. Alternatively, theprocess 200 is fully automated. The process 200 includes a plurality ofblocks 202-220, which are performed consecutively.

In block 202, a selection of a pre-contrast image and a post-contrastimage is made via a computer. The selection can be automatic, such asbased on a heuristic or artificial intelligence (AI). The selection canbe manual, such as via a user input. For example, such selection is madevia a graphical user interface (GUI) running on the computer.

The pre-contrast image depicts, in multiple dimensions, such as threedimensions (3D), an area of a brain, such as a hippocampus, before anintake of a contrast agent, such as a gadolinium solution. Note thatsuch depiction can be grayscale, monochrome, or in color. Additionally,any type of contrasting agent used for brain imaging may be used. Thesecan include a super paramagnetic iron oxide. The post-contrast imagedepicts, in multiple dimensions, such as 3D, the area of the brain, suchas the hippocampus, after the intake of the contrast agent, such as thegadolinium solution. Note that such depiction can be grayscale,monochrome, or in color.

The pre-contrast image and the post-contrast image are based on humansubjects. For example, such subjects can be identified from acommunity-based FMRI study of individuals 65 years and older, whoreceived a detailed neuropsychological and neurological evaluation, andwho were free of Alzheimer's disease dementia, mild Alzheimer's disease,or mild cognitive impairment indicative of a pre-dementia stage ofAlzheimer's disease in this population. For example, the pre-contrastimage can be a FMRI image acquired with a 1.5-T Philips Intera scanner,generating T1-weighted images (lime to repeat, 20 milliseconds (ms):time to echo, 6 ms; flip angle, 25 degrees; in plane resolution, 0.78millimeters (mm)×0.78 mm; slice thickness, 3 mm) acquired perpendicularto a long axis of a hippocampus. The post-contrast image can be a FMRIimage similarly acquired four minutes after an intravenousadministration of the contrast agent gadolinium (0.1 millimoles perliter (mmol) kilogram (kg)⁻¹).

The pre-contrast image and the post-contrast image are stored in a datastore, such as a data structure, for instance, a database, which can berelational. The data store can be local to or remote from the computer,such as via a distributed computing platform. For example, the datastore can be coupled, whether wired or wireless, to the computer suchthat the computer is able to retrieve the pre-contrast image and thepost-contrast image therefrom. Upon selection, the pre-contrast imageand the post-contrast image are retrieved from the data store forprocessing, as described herein. Note that the data store stores thepre-contrast image and the post-contrast image, whether alone or withother images of any type, size, date, or number. Alternatively, thepre-contrast image and the post-contrast image can be stored alone inone data store or distributed among a set of data stores, whether suchdata stores are local or remote from each other. Alternatively, a set ofpointers or references can be stored in the data store, where the set ofpointers or references points to or refers to the pre-contrast image andthe post-contrast image.

Upon such selection, the pre-contrast image and the post-contrast imageare ready for processing, such as via being loaded into a random accessmemory (RAM) of the computer or a processor cache. Alternatively, thepre-contrast image and the post-contrast image are copied, whetherserially or in parallel, and such copies are then ready for processing,which can be useful in order to maintain a trail or a log, therebyavoiding a modification of at least one of the pre-contrast image andthe post-contrast image, which can be original.

In block 204, a determination is made via the computer whether a vesselsegmentation has been performed on the pre-contrast image and thepost-contrast image. For example, the determination determines whetherthe pre-contrast image and the post-contrast image depict a brainvasculature in a sufficiently distinct manner for further imageprocessing. Such determination can be made automatically, such as viathe pre-contrast image and the post-contrast being flagged indicative ofalready performed vessel segmentation and the computer reading suchflags, or via the computer performing machine vision algorithms orpattern recognition algorithms on the pre-contrast image and thepost-contrast image to determine whether the vessel segmentation hasalready been performed. Note that automatic determination can be madelocal to or remote from the computer, such as via a distributedcomputing platform. Such determination can also be made manually, suchas via a user input into the computer based on personal observation ofthe pre-contrast image and the post-contrast image. If the determinationthat the vessel segmentation has been performed, then block 208 isperformed. If the determination that the vessel segmentation has notbeen performed, then block 206 is performed.

In block 206, a vessel segmentation algorithm is performed on thepre-contrast image and the post-contrast image via the computer. Suchperformance can be local to the computer or remote from the computer,such as via a distributed computing platform. The vessel segmentationalgorithm can be automatically triggered, such as via the computerreading a flag indicative of a lack of a vessel segmentation in at leastone of the pre-contrast image or the post-contrast image, or thecomputer machine vision algorithm or the pattern recognition algorithmautomatically triggering the vessel segmentation algorithm based onprocessing. The vessel segmentation algorithm can be manually triggered,such as via a user interfacing with a GUI running on the computer, suchas via activating a visual element on the GUI, such as a button. Thevessel segmentation can be performed via at least one of a patternrecognition technique, a model-based technique, an AI-based technique, aneural network-based technique, or a tubular objection detectiontechnique.

In block 208, the pre-contrast image and the post-contrast image arealigned onto an imaging space. Such alignment can be automated, such asvia the computer identifying, such as via a machine vision algorithm ora pattern recognition algorithm, a set of segmented vessels on thepre-contrast image and the post-contrast image. Note that automaticalignment can be made local to or remote from the computer, such as viaa distributed computing platform. Such alignment can also be manual,such as based on a user request input into the computer, such as via aninput device. The imaging space can be a fluid attenuated inversionrecovery (FLAIR) imaging space, but other types of imaging spaces arepossible. Note that the pre-contrast image and the post-contrast imageare registered on the imaging space. For example, the image registrationis non-linear, but other types of image registration are possible, suchas a label based approach, an intensity based approach, or ahybrid/combination thereof. For example, the image registration isperformed via a non-linear warp/spatial transformation and atransformation matrix, both of which can be saved for subsequent use.Note that, in some embodiments, at least one of an intensitynormalization, an image reorientation, and a brain extraction can occurbefore, during, or after performance of block 208.

In block 210, a subtracted image is generated from the pre-contrastimage and the post-contrast image via the computer. Such subtraction canbe automated, such as via the computer identifying, such as via amachine vision algorithm or a pattern recognition algorithm, a set ofsegmented vessels on the pre-contrast image and the post-contrast image.Note that the automatic subtraction can be made local to or remote fromthe computer, such as via a distributed computing platform. Suchsubtraction can also be manual, such as based on a user request inputinto the computer, such as via an input device. For example, thepre-contrast image is subtracted from the post-contrast image togenerate a raw subtracted volume, i.e. a third image. Intra-subject andintra-modal co-registrations used a symmetric rigid body alignmentincorporating a robust statistics measure. Raw image values of thesubtracted image can be normalized to a mean signal intensity of apatient's superior sagittal sinus. This mean value represents pure bloodvalue of the subtracted image and by normalizing to this value, thepresent disclosure enables a computer to generate an image thatrepresents percent value of blood volume, where the value of the meansignal in the vessel indicates 100%.

In block 212, a vessel is isolated in the subtracted image via thecomputer. The vessel can be a blood vessel, such as an artery, anarteriole, a vein, a venule, or a capillary. Alternatively, the vesselcan be a lymph vessel. Such isolation can be automated, such as via thecomputer receiving an indication of a presence of the subtracted image,such as a message. For example, the isolation can comprise identifyingthe vessel in the subtracted image based on a set of criteria. Note thatthe automatic isolation can be made local to or remote from thecomputer, such as via a distributed computing platform. Such isolationcan also be manual, such as based on a user request input into thecomputer, such as via an input device. For example, the superiorsagittal sinus can be isolated in the subtracted image using a modifiedFrangi vesselness filter and a pre-defined ROI, where the Frangi filteruses 3D eigenvalues of a Hessian matrix of the subtracted image tocalculate a set of global shape parameters of the superior sagittalsinus ROI, including, but not limited to, its anisotropic features. Theisolated image can then be eroded using a standard kernel to ensure thatthe ROI sits entirely in a sinus cavity.

In block 214, a voxel intensity mean value of a segment of the vessel isobtained via the computer. Note that the segment comprises at least aportion of the vessel. The intensity is indicative of blood content orblood flow in the segment of the vessel. The mean value is obtained on avoxel-by-voxel basis for the segment based on a highest voxel intensityin the segment of the vessel, such as, for example, top twenty five (25)percent of the segment, i.e. top 25% of the most illuminated voxels inthe segment of the vessel. Note that such amount is an example and otheramounts can be used, whether greater, such as thirty three (33) percent,or lesser, such as fifteen (15) percent. For example, a cut-off of lessthan top 10%, top 10%, top 11%, top 12%, top 13%, top 14%, top 15%, top16%, top 17%, top 18%, top 19%, top 20%, top 21%, top 22%, top 23%, top24%, top 25%, top 26%, top 27%, top 28%, top 29%, top 30%, top 31%, top32%, top 33%, top 34%, top 35%, top 36%, top 37%, top 38%, top 39%, top40%, greater than top 40%, or any ranges of the foregoing can be used.For example, a mask can be applied to the subtracted image, and ameasure of absolute blood is calculated as a mean of the subtractedimage. Such obtaining can be automated, such as via the computerreceiving an indication of a presence of the vessel in the subtractedimage, such as a message. Note that the automatic obtaining can be madelocal to or remote from the computer, such as via a distributedcomputing platform. Such obtaining can also be manual, such as based ona user request input into the computer, such as via an input device.

In block 216, the subtracted image is divided by the voxel intensitymean value via the computer. Such division is matrix based anddetermines a percentage of CBV in the subtracted image. Accordingly, aCBV map image is formed, which indicates a percentage of blood volume inthe segment. Such division can be automated, such as via the computerreceiving an indication of a receipt of the voxel intensity mean value,such as via a message. Note that the automatic division can be madelocal to or remote from the computer, such as via a distributedcomputing platform. Such division can also be manual, such as based on auser request input into the computer, such as via an input device.

In block 218, a filter is applied to a resulting image, i.e., the CBVmap, via the computer. Such filtering filters out large vasculature,such as entorhinal cortex vasculature, and filters in smallervasculature, which is desired. For example, to rule out various effectsresulting from large vessels, several methods of vessel filtering can beused, such as fitting a bimodal Gaussian curve to a histogram of data orperforming an expectation-maximization segmentation. Such applicationcan be automated, such as via the computer receiving an indication of aformation of the CBV map, such as via a message. Note that the automaticapplication can be made local to or remote from the computer, such asvia a distributed computing platform. Such application can also bemanual, such as based on a user request input into the computer, such asvia an input device.

In block 220, a filter mask is generated based on the application of thefilter to the resulting image via the computer. The filter mask is basedon intensity values of the filter. The filter mask can be a binary mask,which can then be applied to the generated CBV image. The CBV image canbe mapped according to changes in a transverse relaxation time (ΔR2)induced by an injection of the gadolinium solution, where the CBV imagecan be derived by normalizing ΔR2 to a mean ΔR2 signal present in aninternal jugular vein, as delineated by a blinded rater.

For example, to process the CBV image, such as to determine contrastuptake and scan, the pre-contrast image and the post-contrast image arehigh resolution MRI images which focus on what is referred to as T1weighting, or a spin-lattice relaxation time. This time varies fromtissue to tissue, where population of protons generates resonantfrequencies depending on a tissue molecular composition. For example,white matter has a different spin-lattice relaxation time compared tocerebrospinal fluid. For a standard MRI sequence, T1 properties,specific time to echo (TE), and time to repetition (TR) are adjusted tooptimize an acquisition of such scans as well as an amount of time ascan should take. In one example, a spoiled T1 weighted gradient echoimage was acquired with a sub-millimeter resolution in-plane of anoblique hippocampus (0.68*0.68*3 mm). Such scan was obtained prior toand after an intravenous bolus injection of a tracer agent. One type oftracer agent is a chelated form of gadolinium at a prescribed dosage.Some effects of such tracer agent have been studied, and this traceragent is known for an ability not to cross a blood brain barrier of thepatient, while increasing contrast of a brain vasculature. A gadoliniumion in the tracer agent acts as a paramagnet in a main coil of an MRImagnet. As such, when exposed to an external magnetic field, thegadolinium ion impacts a local relaxation rate of protons in proximityof the gadolinium ion. An effect this susceptibility has on influencingprotons (and therefore on an MRI signal) of surrounding tissues isorders of magnitude higher than a paramagnetic or ferromagnetic effectfrom endogenous contrast sources in the brain, such as deoxygenatedhemoglobin or components of tissue, such as hemosiderin or hemoglobin.Accordingly, using previously defined timing constraints, a set ofidentical imaging sequences is acquired, such as two. Each of thesequences has a T1 weighting before and after an intravenous injectionof the contrast agent. After conclusion of the intravenous injection ofthe contrast agent, a time period of four (4) minutes is set prior to astart of a second part of the acquisition. This time gap allows for anadequate perfusion through a circulatory system of the patient. Notethat there can be a preservation of the blood brain barrier, where theblood brain barrier acts as a filter that only certain molecules andmetabolites can pass through to tissues in a capillary bed, whereasothers recirculate through the brain. In cases of tumor growth orhemorrhages, there may be a breakdown of the blood brain barrier, inwhich case one method described herein may not as adequately detect amicrovasculature of the brain.

One way to measure whether MRI signal has been adequately increasedthroughout the brain is to examine histograms of both pre-contrast imageand the post-contrast image. As shown in FIGS. 3A and 3B, by subtractinga pre-contrast image one dimensional histogram (with fixed binning) froma post-contrast image one dimensional histogram, a histogram ofsubtracted values is obtained. Such histogram can be evaluated to see ifthere is accurate ‘uptake’ of the contrast agent.

In FIG. 3A, where an X-axis corresponds to a voxel intensity and aY-axis corresponds to a count of signal intensities, a sample plotdiagram is depicted, where the sample plot diagram is generated for thepre-contrast image (indicated by ‘x’) and the post contrast image(indicated by ‘*’). Such plot diagram representatively graphs adifference of the pre-subtracted image histogram from thepost-subtracted image histogram. In FIG. 3B, where an X-axis correspondsto signal intensities and a Y-axis corresponds to histogram counts, thesample plot diagram of FIG. 3A is fit to a distribution, such as asecond degree Gaussian distribution. Doing so yields a positive mean fora higher intensity distribution (indicated a net increase in signal inthe post-contrast image) and a negative mean for a lower meandistribution. This mean is used to compare to an existing distributionto determine if, for instance, there is lack of significant contrastuptake, or other contrast enhancing related abnormalities that wouldalter either curves significantly.

Further, to generate the CBV image, such as to identify a blood vessel,a location in the brain is identified, where the location identifies avessel having a flow of ‘pure blood’ in the brain. This location is usedas a denominator in later calculations. ‘Pure blood’ is defined as amean value of signal from an area known to be pure blood, i.e. thesuperior sagittal sinus. The location of this mask is used as a sampleof points, most or all of which have a value. The computer thencalculates the mean values of all of the subtracted values inside thisvessel and determines a total mean value. This mean value is a numberthat is then used for the denominator. This vessel is identified byanalyzing the post-contrast image, which is a conventional ‘structuralwith contrast’ MRI image with clear enhanced vasculature, in a 3D matrixform. This image is loaded into the 3D matrix form, and a Hessian matrixform of the 3D matrix of numbers is calculated. The Hessian matrix is asquare matrix of second order derivatives. The parameters that definesuch eigenvalues determine a local curvature of large value differenceinside the post-contrast image. Depending on these eigenvalue voxelvalues, a map is made with a set of particular pre-defined eigenvalueratios. This map is called a ‘vesselness’ function. Once these ratiosare thresholded to a specific number and parameter, the computer canobtain local voxel probabilities for plate, tubular, or sphericalstructures. Values are chosen that correspond to small (i.e.approximately <1 cm) diameter vessels, such as the superior sagittalsinus, which is seated within a cannula above a falx cerebri of thebrain. A grayscale implementation of such output is saved as a grayscaleimage and the grayscale image is binarized so that only areas whereprospective vessels are positioned is masked. Several morphologicaloperations, such as erosion or dilation, are performed on the grayscaleimage and a connected component algorithm is performed to assign eachmasked region that consists of contiguous vessels its own unique set ofnumbers in the post-contrast image.

FIGS. 4A and 4B show an example of this connected component system. FIG.4A shows an example embodiment of an image with several connectedcomponents which are unlabeled according to the present disclosure. InFIG. 4A, potential vessels are shown, with each of the shown structurescontaining an identical fixed value. FIG. 4B shows an example embodimentof an image with several connected components which are labeled based oncontiguous voxels according to the present disclosure. In FIG. 4B, thepotential vessels are shown after a connected component system has beenapplied. Therefore, each contiguous structure contains uniform values,but those values change from one structure to another.

Accordingly, a probabilistic atlas corresponds to a set of existingmanually corrected sagittal sinus masks. The probabilistic atlas ispositioned in a standard space, such as the space of an existingcanonical atlas. The probabilistic atlas is used as a reference. A meanvalue of each of the connected component masks is multiplied by theprobabilistic atlas, voxel by voxel, and a mean value is generated foreach voxel value. A largest mean value for a connected component maskcorresponds to an area most likely to include the superior sagittalsinus in a new image (since all images are in co-registered space). Thismask is applied to the subtracted image (generated from a co-registeredpre-contrast image subtracted from a post-contrast image) and a set oftop remaining voxels, such as, for example, 25% based on brightness, areconsidered to constitute the vessel mask, and from these voxels a meanvalue is generated. Other percentages may be used, such as, for example,less than 10%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%,21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%,35%, 36%, 37%, 38%, 39%, 40%, greater that 40% may be used. Therefore,as shown in FIGS. 5A-5D, a frontal, a side, a top, and an orthographicviews are depicted respectively. Each of the views illustrates a rawunbinarized vesselness mask from various perspectives. Likewise, asshown in FIGS. 6A-6D, a frontal, a side, a top, and an orthographicviews are depicted respectively. Each of the views in FIGS. 6A-6Dillustrates a connected component mask of FIGS. 5A-5D from variousperspectives, after thresholding, i.e., FIGS. 5A-6D depict anarchetypical image. Similarly, FIG. 7 depicts a ranking of likelihood ofan isolated mask compared to a probabilistic atlas. Moreover, as shownin FIGS. 8A-8C, a frontal, a side, and a top views are depictedrespectively. Each of the views in FIGS. 8A-8C illustrates aprobabilistic atlas in a standard space, from a different perspective.

The superior sagittal sinus is used to capture a bulk ofcerebrovasculature located within one specific area of cerebrum. Inorder to obtain a measurement of ‘pure blood’ in the subtracted image, aseries of segmented and manually curated superior sagittal sinus masksare calculated. All relevant images are in a group template space suchthat the superior sagittal sinus is in one stereotactic space. The grouptemplate space is generated using the pre-contrast images, which issimilar to structural T1 images and provide adequate regionalinformation desired to both not only to generate an accurate anatomicalaverage template of the sample brains, but also to perform an accurateco-registration calculation. Although a type of co-registration canvary, a diffeomorphic, which can be similar to that found in an AdvancedNormalization Toolkit (ANTS) software package, can be used. This grouptemplate is a “space” to which a connected component thresholded imagecan be co-registered. FIGS. 9A-9C depict an example of an vessel atlasimage on a template brain from a frontal view, a side view, and a topview respectively.

FIG. 10 shows an example embodiment of an atlas generation and atlasapplication diagram according to the present disclosure. To generate anatlas, a group of pre-contrast and post-contrast images are generatedfrom a population of subjects that match certain criteria for a standardpopulation. This standard population can include subjects sharingsimilar ethnicity, age, and gender. For each individual from that group,an individual pre-contrast image (a) is subtracted from an individualpost-contrast image (b) to obtain a subtracted image, as describedherein. A superior sagittal sinus depiction is calculated from avesselness map (c) based on the subtracted image, as described herein. Atemplate is grouped from a set of pre-contrast images (d) and an averagesuperior sagittal nerve map is formed (e) based on calculating thesuperior sagittal sinus depiction (c), as described herein, such asprobabilistically. Note that a template space is based on an averagebrain collection of data of the group. To apply an atlas to a newsubject, an individual pre-contrast image is applied onto the template(f), as described herein, which can be via block (h). Assuming anindividual pre and post contrast image are in the same space, then thepre-contrast image is co-registered to the group template space, and thetransformation (a matrix file containing transformation information) isapplied to the individual post-contrast image, thus placing in the spaceof the template (g) and (h), as described herein. A connected componentvesselness map is formed (i) based on the individual post-contrast image(g). The group template (d) is processed via transformation onvesselness map (j) on the template space. An output of suchtransformation (j) and an output of connected component vesselness map(i) is used to determine best or optimal vessel in connected componenton a superior sagittal sinus nerve atlas (k) based on block (e). Notethat what is best or optimal is determined based on looking foroverlaps, where the best or optimal vessel is multiplied by the superiorsagittal sinus atlas with each connected component mask beingrepresented as (1,0). The same connected component is used in subjectspace vesselness map to isolate a vessel (L).

For optimization parameters, note that several parameters can be usedthat can be tailored to a specific goal or an imaging requirement.First, images are acquired with a predefined sequence parameter, afairly routine T1 weighted structural MRI scan. The sequence isdeveloped with considerations for best capturing an anatomy of a longcircuit of the hippocampus. Some fundamental factors of the MRIsequence, such as time to repetition (TR) and time to echo (TE), aremeant to reduce a time to acquire the scan. Such parameters could beapplied to different scanner types, however, CBV images can be madefunctionally from any T1 weighted scan within appropriate parameters. Ifa new scanner is used to acquire CBV images, then a phantom could beused and a set of values can be compared to in order to ensurereliability across sites. Phantom refers to a custom device thatcontains a known number of permanent items known to show up in an MRIscan that can be scanned repeatedly without change in signal. Toaccomplish such configuration, a phantom can be used on each machine tocompare signal intensities and geometries. For purposes of developingthis sequence, an adapted Spoiled Gradient Echo (SPE) can be used. SPEuses a standard gradient echo sequence with a component that ‘spoils’ oruses either radiofrequency (RF) pulses or gradients to greatly reduceunwanted effects of transverse magnetization.

In order to identify a vessel cutoff, i.e. filtering, since at leastsome of valuable information of the CBV map comes from identificationchanges present in small vasculature of the cerebrum, and notnecessarily large or epicortical vessels, there may sometimes be a needto filter those larger vessels. Since these vessels occur throughout thebrain and not just encapsulating a periphery thereof, at least somevalues of high signal would need to be identified so that the highsignal does not significantly influence the smaller region of interestanalysis. The smaller (<<1 mm) vasculature makes up a bulk of thecerebrum, but for other purposes where the contrast agent is used in aclinical setting, identification of most or substantially all vessels.Large vessels are easily identifiable as the cutoff is determined byfitting a 2nd degree Gaussian curve to the signal of the non-zero CBVmap voxels. Although, a cutoff of ten (10) percent or some other amountsuch as, for example, any percentage from 5% to 25% or greater can beapplied, however, a fitted Gaussian curve can more accurately determinea gross high intensity signal cutoff and exclude such cutoff fromanalysis. FIG. 11A shows a raw CBV image mapped on an anatomical space.FIG. 11B shows a cortical segmentation mask, which is labeled based onregions according to anatomical locations on the anatomical space. FIG.11C shows the cutoff applied to the cortical segmentation mask, wherethe CBV map is illustrated with vessel filters on the anatomical space.A red arrow indicates an area of increased signal that is filtered (i.e.no longer shows up in the masked image) after the cutoff is applied.Note that this corresponds to the block 220 of FIG. 2.

For CBV image generation, once the vessel mask on the post contrastimage has been identified, a first step to generate the CBV map is tocreate a median mask of the post-contrast image and the pre-contrastimage. This places both images in one stereotactic space, and accountsfor head movement between first and second acquisition. Next, asubtracted image is generated. This subtracted image is then divided bythe value of pure blood, as previously defined as a mean value of signalfrom an area known to be pure blood, i.e. the superior sagittal sinus. Aresulting map then has the aforementioned filtering method applied toremove large, high signal vessels.

For sample size and findings based on patient samples, see FIG. 12,which applies to a parametrically derived template using a voxel basedanalysis. In FIG. 12, values for each subject group came from abilateral region of interest in a lateral entorhinal cortex in ahippocampal formation. This region and a corresponding space of thisregion can be applied to a new CBV scan and determine mean value in thatregion. Note that several statistical methods were performed todetermine results using CBV data. These include 2×2 factorial analyses,one-way analysis of variance (ANOVA) and group comparisons. These datamay be may performed on either voxel (a smallest reconstructed unit ofan MRI image) or a region based analysis. In determining regionaldifferences in the CBV data, a raw CBV signal with parametric analysesand mean CBV signal with regional analysis and mean CBV values inpre-defined regions (confined to pre-defined spaces) was examined.

FIG. 13 shows a perspective view of an example embodiment of a CBV ROIdiscriminating between controls and preclinical Alzheimer' patientsaccording to the present disclosure. FIG. 13 supports a determination ofa utility of a different CBV ROI in being able to discriminate betweencontrols and preclinical Alzheimer's disease. Although a regionindicated is the entorhinal cortex, which is an area implicated in apathophysiology of a disease, an identical approach would apply inpatients with psychosis who may convert to schizophrenia. Instead ofthis region, a focus would be near the CA1/subiculum sub-regions of thehippocampus, as increased metabolism in this region as detected with CBVis correlated to conversion to psychosis.

In one aspect, in terms of patient population or monitoring, a focus ona neurological or psychiatric disorder entails using study data toestablish interval plots, such as for aging patients, or pre-definedranges for prescribed groups. Another potential approach entailsdetermination of a “cut-off”, a value above which or below which CBVwould be considered to be “positive”.

In some embodiments, a computer can run software which can display anexample set of imaging scans which depict a calculation of hippocampalvolume which correlates to Alzheimer's disease risk according to thepresent disclosure. Accordingly a set of graphs can be generated, wherethe set of graphs can indicate a left and a right hippocampus volumes incontrast to left and right inferior lateral ventricle.

FIG. 14 shows a flowchart of an example embodiment of a process forimage processing according to the present disclosure. A computer systemconfigured according to a technology described herein, but at least withdifferent correlation parameters, a decrease in time for a preclinicaldiagnosis of schizophrenia can be accomplished. In that regard, inprocess 1600, a patient 1602 can arrive at an MRI site 1604, where afteran MRI is generated, such data can be processed using an algorithmdescribed herein. In some embodiments, process 1600 can takeapproximately 10 hours to produce a set of CBV values that then can beused to predict onset of psychosis, such as schizophrenia. For example,software can be installed on a standard personal computer for localprocessing, such as via a local option where the CBV is processedlocally 1608. Alternatively, the software could be run on a largercomputer such as a mainframe, such as in a hospital, with access via aterminal, such as a terminal in a hospital, such as via a local optionwhere the CBV is processed locally 1608. Yet further, the software couldbe embedded in or incorporated into an MRI scanning system so that theanalysis occurs at the MRI facility, such as via a local option wherethe CBV is processed locally 1608. Yet further, the software could berun on servers, such as encrypted servers using Amazon EC2, such as viaa local option where the CBV is processed remotely 1606. One advantageof processing of such data via remote servers (cloud computing) enablesthe processing to occur on more powerful machines, while the result canbe made available via less powerful devices such as so-called dumbterminals. Whether CBV is processed locally 1608 or remotely 1606, areport can be generated 1610 based on such processing. Such generationcan be local or remote.

FIG. 15A shows a perspective view of an example embodiment of a desktopcomputer according to the present disclosure. A desktop computer 1700 isused for image processing, as described herein. However, note that othertypes of computers or computer systems can also be configured for imageprocessing, as described herein. For example, such computer can be aworkstation computer, a laptop computer, a terminal computer, a tabletcomputer, a mobile phone, a server computer, a medical imaging machine,a cloud-computing system, a mainframe, a supercomputer, or othersuitable computers.

FIG. 15B shows a schematic view of an example embodiment of a computeraccording to the present disclosure. The computer 1700 comprises aprocessing unit 1702, a memory unit 1704 operably coupled to theprocessing unit 1702, a graphics unit 1706 operably coupled to theprocessing unit 1702, a networking unit 1708 operably coupled to theprocessing unit 1702, and a display unit 1710 operably coupled to theprocessing unit 1702. The computer 1700 is powered via mainselectricity, such as via a power cable. In other embodiments, thecomputer 1700 is powered via at least one of an onboard rechargeablebattery, such as a lithium-ion battery, and an onboard renewable energysource, such as a photovoltaic cell or a hydropower turbine. Note thatthe computer 1700 can be operably coupled to at least one user inputdevice, such as a computer keyboard, a computer mouse, a touchpad, atouchscreen, or other suitable user input devices.

The processing unit 1702 comprises a hardware processor, such as amulticore processor. For example, the processing unit 1702 comprises acentral processing unit (CPU).

The memory unit 1704 comprises a computer-readable storage medium, whichcan be non-transitory. The medium stores a plurality ofcomputer-readable instructions for execution via the processing unit1702. The instructions instruct the processing unit 1702 to facilitateperformance of a method for diagnosis of a neurological or a psychiatricillness, as described herein. Some examples of the memory unit 1704comprise a volatile memory unit, such as random access memory (RAM)unit, or a non-volatile memory unit, such as an electrically addressedmemory unit or a mechanically addressed memory unit. For example, theelectrically addressed memory comprises a flash memory unit. Forexample, the mechanically addressed memory unit comprises a hard diskdrive. The memory unit 1704 is in wired communication with theprocessing unit 1702.

The graphics unit 1706 comprises a graphics processing unit (GPU) forimage processing. The graphics unit 1706 is a graphics dedicated unit,but in other embodiments, the processing unit 1702 is integrated withthe graphics unit 7106. For example, the graphics unit 1706 comprises avideo card. The graphics unit 1706 is in wired communication with theprocessing unit 1702.

The networking unit 1708 comprises a network interface controller forcomputer network communication, whether wired or wireless. For example,the networking unit 1708 comprises a hardware unit for computernetworking communication based on at least one standard selected from aset of Institute of Electrical and Electronics Engineers (IEEE) 802standards, such as an IEEE 802.11 standard. For instance, the networkingunit 1708 comprises a wireless network card operative according to aIEEE 802.11(g) standard. The networking unit 1708 is in wiredcommunication with the processing unit 1702.

The display unit 1710 comprises a display for displaying information.The display comprises at least one of an electronic visual display, aflat panel display, a liquid crystal display (LCD), an electrophoreticdisplay, and a volumetric display. For example, the display unit 1710comprises a touch-enabled computer monitor. The display unit 1710 is inwired communication with the processing unit 1702.

In one mode of operation, the computer 1700 runs such that a method fordiagnosis of a neurological or a psychiatric illness, as describedherein, is performed, such as based on receiving a user request inputvia the display unit 1710. The computer 1700 displays a result of themethod via the display unit 1710 based on operation of the graphics unit1706. Optionally, the computer 1700 communicates the result to anothercomputer over a computer network via the networking unit 1708, such asbased on receiving a user request input via the display unit 1710.

In another mode of operation, the computer 1700 runs such that a methodfor diagnosis of a neurological or a psychiatric illness, as describedherein, is performed, such as based on receiving a remotely input userrequest. The computer 1700 communicates the result to a user computerover a computer network via the networking unit 1708, such as based onreceiving a remotely input user request. Such mode of operation can bebased on a cloud computing model.

FIG. 16 shows an example screenshot of a software application forcerebral blood volume generation according to the present disclosure.The screenshot depicts a processing interface for generating CBV voxelvalues. As can be seen, in addition to a conventional brain imagingsoftware package processing, such as FreeSurfer processing, there is aselection for use of a gadolunium paramagnetic contrast agent to produceother results in the T1 space.

In some embodiments, various functions or acts can take place at a givenlocation and/or in connection with the operation of one or moreapparatuses or systems. In some embodiments, a portion of a givenfunction or act can be performed at a first device or location, and theremainder of the function or act can be performed at one or moreadditional devices or locations.

In some embodiments, an apparatus or system comprise at least oneprocessor, and memory storing instructions that, when executed by the atleast one processor, cause the apparatus or system to perform one ormore methodological acts as described herein. In some embodiments, thememory stores data, such as one or more structures, metadata, lines,tags, blocks, strings, or other suitable data organizations.

As will be appreciated by one skilled in the art, aspects of thisdisclosure can be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure can take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or asembodiments combining software and hardware aspects that can allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the disclosure can take the form of a computerprogram product embodied in one or more computer readable medium(s)having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) can beutilized. The computer readable medium can be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium can be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific example (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium can be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium can include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal can takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium can be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium can be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure can be written in any combination of one or moreprogramming language, including an object oriented programming language,such as Java, Smalltalk, C++ or the like and conventional proceduralprogramming language, such as the “C” programming language or similarprogramming languages. The program code can execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer can be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection can be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the form disclosed. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope and spirit of the disclosure. The embodiments were chosen anddescribed in order to best explain the principles of the disclosure andthe practical application, and to enable others of ordinary skill in theart to understand the disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the steps (or operations) described thereinwithout departing from the spirit of the disclosure. For instance, thesteps can be performed in a differing order or steps can be added,deleted or modified. All of these variations are considered a part ofthe disclosure. It will be understood that those skilled in the art,both now and in the future, can make various improvements andenhancements which fall within the scope of the claims which follow.

1-30. (canceled)
 31. A method comprising: identifying, via a processor,a brain vessel segment in a subtracted image; determining, via theprocessor, a voxel intensity mean value of the brain vessel segmentbased on a highest voxel intensity in the brain vessel segment such thatthe voxel intensity mean value indicates at least one of a content of ablood in the brain vessel segment or a flow of the blood in the brainvessel segment; dividing, via the processor, the subtracted image by thevoxel intensity mean value such that a resulting image is obtained andsuch that a percentage of a volume of the blood in the brain vesselsegment of the resulting image is determinable; applying, via theprocessor, a filter onto the resulting image; and generating, via thecomputer, a mask based on the filter being applied to the resultingimage.
 32. The method of claim 31, wherein the voxel intensity meanvalue indicates the content of the blood in the brain vessel segment.33. The method of claim 31, wherein the voxel intensity mean valuesindicates the flow of the blood in the brain vessel segment.
 34. Themethod of claim 31, wherein the filter is a first filter, wherein thebrain vessel segment is identified based on a second filter and apre-defined region of interest.
 35. The method of claim 34, wherein thebrain vessel segment is of a superior sagittal sinus.
 36. The method ofclaim 31, wherein the voxel intensity mean value is obtained on avoxel-by-voxel basis for the brain vessel segment based on the highestvoxel intensity in the brain vessel segment.
 37. The method of claim 31,wherein the mask is a first mask, wherein the voxel intensity mean valueis based on a second mask being applied to the subtracted image and ameasure of an absolute blood is determined as a mean of the subtractedimage.
 38. The method of claim 31, wherein the subtracted image isdivided by the voxel intensity value based on a matrix.
 39. The methodof claim 31, wherein the resulting image is a cerebral blood volume map.40. The method of claim 31, wherein the filter filters out a first setof vasculature and filters in a second set of vasculature, wherein thefirst set of vasculature has a first vasculature, wherein the second setof vasculature has a second vasculature, wherein the first vasculatureis larger than the second vasculature.
 41. The method of claim 31,wherein the filter filters based on fitting a bimodal Gaussian curve toa histogram of data.
 42. The method of claim 31, wherein the filterfilters based on performing an expectation-maximization segmentation.43. The method of claim 31, wherein the mask is based on an intensityvalue of the filter.
 44. The method of claim 31, wherein the mask is abinary mask.
 45. The method of claim 31, wherein the brain vesselsegment is identified based on a vesselness filter and a pre-definedregion of interest, wherein the vesselness filter filters based on a setof eigenvalues of a Hessian matrix of the subtracted image, wherein thesubtracted image is modified such that the region of interest ispositioned in a predefined area.
 46. The method of claim 31, wherein thevoxel intensity mean value is based on a highest voxel intensity rangein the brain vessel segment, wherein the highest voxel intensity rangecomprises a top 40% of voxel intensities.
 47. The method of claim 46,wherein the highest voxel intensity range comprises a top 33% of voxelintensities.
 48. The method of claim 46, wherein the highest voxelintensity range comprises a top 25% of voxel intensities.
 49. The methodof claim 31, further comprising: causing, via the processor, a formationof a set of diagnosis data for a neurological illness based on theresulting image, wherein the neurological illness is of a patientassociated with the brain vessel segment.
 50. The method of claim 31,further comprising: causing, via the processor, a formation of a set ofdiagnosis data for a psychiatric illness based on the resulting image,wherein the psychiatric illness is of a patient associated with thebrain vessel segment.